LLMS ARE NOT THE BLACK BOX YOU WERE PROMISED

Summary from the TLDR Dev newsletter

Recent advancements in mechanistic interpretability allow researchers to reverse-engineer LLMs, moving them beyond the "black box" stage by decomposing neural activations into interpretable features. This capability to map internal logic provides new opportunities for steering model behavior, identifying risks, and developing more effective learning architectures.